add patience for early stopping or infinit epochs

1 files changed, 74 insertions, 0 deletions

diff --git a/src/dataloader.py b/src/dataloader.py
index 64ce7f7..ebe8d3a 100644
--- a/src/dataloader.py
+++ b/src/dataloader.py
@@ -185,6 +185,80 @@ def get_diffusion_dataloader(
     )
 
 
+def get_diffusion_dataloaders(
+    predict_x0: bool = True,
+    batch_size: int = 64,
+    shuffle: bool = True,
+    num_workers: int = 4,
+    autoencoder: AEModule | None = None,
+    val_split: float = 0.1,
+    test_split: float = 0.1,
+    seed: int = 42,
+) -> tuple[DataLoader, DataLoader, DataLoader]:
+    """
+    Create DataLoaders for diffusion training, validation, and testing.
+
+    Args:
+        predict_x0: If True, model predicts x0. Otherwise predicts noise.
+        batch_size: Batch size
+        shuffle: Whether to shuffle training data
+        num_workers: Number of data loading workers
+        autoencoder: Optional autoencoder for latent diffusion
+        val_split: Fraction of data for validation
+        test_split: Fraction of data for testing
+        seed: Random seed for deterministic split
+
+    Returns:
+        Tuple of (train_loader, val_loader, test_loader)
+    """
+    if val_split < 0 or test_split < 0 or (val_split + test_split) >= 1:
+        raise ValueError("val_split and test_split must be >= 0 and sum to < 1")
+
+    mnist = get_mnist_dataset(train=True)
+
+    if predict_x0:
+        dataset = DiffusionDatasetX0(mnist, autoencoder)
+    else:
+        dataset = DiffusionDataset(mnist, autoencoder)
+
+    total_len = len(dataset)
+    val_len = int(total_len * val_split)
+    test_len = int(total_len * test_split)
+    train_len = total_len - val_len - test_len
+
+    if train_len <= 0:
+        raise ValueError("Split sizes result in empty training set")
+
+    generator = torch.Generator().manual_seed(seed)
+    train_set, val_set, test_set = random_split(dataset, [train_len, val_len, test_len], generator=generator)
+
+    train_loader = DataLoader(
+        train_set,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        num_workers=num_workers,
+        persistent_workers=True if num_workers > 0 else False,
+    )
+
+    val_loader = DataLoader(
+        val_set,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        persistent_workers=True if num_workers > 0 else False,
+    )
+
+    test_loader = DataLoader(
+        test_set,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        persistent_workers=True if num_workers > 0 else False,
+    )
+
+    return train_loader, val_loader, test_loader
+
+
 def get_autoencoder_dataloader(
     batch_size: int = 64,
     shuffle: bool = True,